A method and system for applying organic fertilizer

Abstract

This invention relates to the field of intelligent fertilization technology, and discloses an organic fertilizer application method and system. The method includes: acquiring crop nutrient requirement data, soil nutrient supply data, and nutrient supply data and mineralization characteristic parameters of candidate organic fertilizers for a target field; performing dynamic nutrient balance analysis on the crop nutrient requirement data and soil nutrient supply data to obtain nutrient demand gap data; based on the mineralization characteristic parameters and soil environmental factors in the soil nutrient supply data, deducing the release rate and cumulative release amount of nutrients in the candidate organic fertilizers as they transform into available forms over time, to fit the nutrient release dynamic curve data of the candidate organic fertilizers; performing spatiotemporal matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing; adapting and adjusting the input amount and application time of the candidate organic fertilizers; determining the specific fertilization schedule and integrating it into an organic fertilizer application scheme. This invention can improve the efficiency of precise organic fertilizer application.

Description

Technical Field

[0001] This invention relates to the field of intelligent fertilization technology, and in particular to an organic fertilizer application method and system. Background Technology

[0002] In the field of intelligent fertilization technology, traditional organic fertilizer application methods often lack dynamic integrated analysis of crop nutrient requirements, soil nutrient supply data, and the characteristics of organic fertilizers themselves. Most existing technologies fail to effectively correlate the mineralization characteristics of organic fertilizers with soil environmental factors, making it difficult to accurately predict the release patterns of nutrients as they transform into available forms over time. This leads to biases in the assessment of the nutrient supply capacity of organic fertilizers, failing to provide accurate basic data support for fertilization, and consequently affecting the targeting and effectiveness of fertilization programs.

[0003] Meanwhile, current fertilization processes lack in-depth analysis of the spatiotemporal matching between nutrient demand gaps and the dynamic release of organic fertilizer nutrients. They fail to fully consider the differences in nutrient requirements at different crop growth stages and critical nutrient demand windows, and lack adaptation to dynamic factors such as real-time soil moisture and weather conditions. This results in insufficient rationality in the determined application rates and timing, not only reducing the efficiency of crop nutrient absorption and utilization but also potentially leading to resource waste or soil ecological imbalance. Overall, the efficiency and scientific rigor are insufficient to meet the development needs of precision fertilization in modern agriculture. Summary of the Invention

[0004] This invention provides an organic fertilizer application method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an organic fertilizer application method, comprising:

[0006] S1. Obtain crop fertilizer requirements data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers for the target field.

[0007] S2. Perform dynamic nutrient balance analysis on the crop fertilizer requirement data and the soil fertilizer supply data to obtain the nutrient demand gap data of the target crop during its growth cycle;

[0008] S3. Associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer.

[0009] S4. Perform spatiotemporal matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer.

[0010] S5. Adjust the amount and timing of application of the candidate organic fertilizer according to the optimal application rate and the timing of fertilization.

[0011] S6. Based on the adjusted input amount and application time, determine the specific fertilization arrangement for the target field and integrate it into an organic fertilizer fertilization plan for the target field.

[0012] In a preferred embodiment, the step of performing dynamic nutrient balance analysis on the crop nutrient requirement data and the soil nutrient supply data to obtain nutrient gap data of the target crop during its growth cycle includes:

[0013] According to the growth cycle of the target crop, the crop fertilizer requirement data of the target field is divided into stage fertilizer requirement data for different growth stages;

[0014] Based on the soil physicochemical properties parameters in the soil fertility data of the target field, the basic nutrient supply capacity of the soil in the target field is evaluated to generate basic fertility intensity data of the soil in the target field at different growth stages.

[0015] The difference between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data is compared to analyze the nutrient supply and demand differences between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data in different growth stages, so as to obtain the stage nutrient requirement data of the target crop.

[0016] By integrating the nutrient requirement data for each stage, the nutrient gap data for the target crop during its growth cycle is obtained.

[0017] In a preferred embodiment, the step of assessing the soil basic nutrient supply capacity of the target field based on the soil physicochemical property parameters in the soil fertility data, to generate soil basic fertility intensity data corresponding to different growth stages of the target field, includes:

[0018] The soil fertility supply data of the target field is analyzed to obtain the soil physicochemical property parameters of the soil fertility supply data;

[0019] The soil physicochemical properties parameters are dynamically correlated with the growth stages of the target field to determine the nutrient supply influencing factors of the target field at different growth stages.

[0020] Based on the nutrient supply influencing factors, the soil physicochemical properties parameters are comprehensively evaluated to generate initial soil fertility potential data for the target field at different growth stages.

[0021] Based on the historical management records of the target field, the initial soil fertility potential data is corrected to obtain the basic soil fertility intensity data of the target field at different growth stages.

[0022] In a preferred embodiment, the step of associating the mineralization characteristic parameters with soil environmental factors in the soil fertility data, and extrapolating the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available forms over time, to fit the nutrient release dynamic curve data of the candidate organic fertilizer, includes:

[0023] A coupling relationship analysis was conducted between the mineralization characteristic parameters in the candidate organic fertilizers and the soil environmental factors in the soil fertility data to determine the dominant environmental factors affecting the nutrient mineralization process of the target field and their corresponding influence weights.

[0024] Based on the mineralization characteristic parameters, the dominant environmental factors and their corresponding influence weights, the mineralization and decomposition process of the candidate organic fertilizer in the soil environment of the target field over time is simulated to obtain the available nutrient concentration data of the candidate organic fertilizer.

[0025] Based on the available nutrient concentration data, the instantaneous release rate and cumulative release amount of nutrients in the candidate organic fertilizer at different discrete time points are deduced;

[0026] The instantaneous release rate data and the cumulative release amount data are subjected to nonlinear fitting to obtain the nutrient release dynamic curve data of the candidate organic fertilizer.

[0027] In a preferred embodiment, the step of performing spatiotemporal matching analysis between the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer includes:

[0028] Based on the growth stage of the target crop, the nutrient demand gap data is decomposed into a time dimension to generate stage nutrient demand data corresponding to different growth stages of the target crop.

[0029] By analyzing the nutrient release dynamic curve data according to the same time dimension as the nutrient requirement data of the aforementioned stage, the expected organic fertilizer nutrient supply data corresponding to the target crop at different growth stages can be obtained.

[0030] The nutrient requirement data for each stage is compared and analyzed with the corresponding expected organic fertilizer nutrient supply data to obtain the nutrient supply and demand difference data of the target crop at different growth stages.

[0031] Based on the nutrient supply and demand difference data, the nutrient supply effect of the candidate organic fertilizer at the preset application rate is evaluated, and the preset application rate is adjusted according to the nutrient supply effect to obtain the preliminary application rate data of the candidate organic fertilizer.

[0032] Identify the growth stage with the most significant differences in the nutrient supply and demand data, and define the growth stage as the critical nutrient requirement window period for the target crop;

[0033] Based on the preliminary application data and the critical fertilizer requirement window, the optimal application rate and timing of the candidate organic fertilizer are determined.

[0034] In a preferred embodiment, determining the optimal application rate and timing of the candidate organic fertilizer by combining the preliminary application rate data with the critical fertilizer requirement window includes:

[0035] Based on the nutrient supply and demand difference data corresponding to the critical fertilizer demand window, determine the amount of nutrient gap that needs to be supplemented by the initial application amount data during the critical fertilizer demand window;

[0036] Based on the nutrient content data of the candidate organic fertilizer and the expected nutrient release rate data of the candidate organic fertilizer during the critical nutrient demand window, the incremental amount of organic fertilizer used by the candidate organic fertilizer to fill the nutrient gap is determined.

[0037] The incremental organic fertilizer is allocated to the initial application data to obtain the optimal application rate of the candidate organic fertilizer;

[0038] Based on the optimal application rate and the time required for nutrient release to reach the effective supply peak in the nutrient release dynamic curve data, the starting point of the critical fertilizer demand window is shifted forward to determine the fertilization timing of the candidate organic fertilizer.

[0039] In a preferred embodiment, adjusting the input amount and application time of the candidate organic fertilizer according to the optimal application rate and the fertilization timing includes:

[0040] Acquire real-time soil moisture data of the target field and meteorological forecast data of the target crop during its growth cycle;

[0041] The real-time soil moisture data is correlated with the optimal application rate to analyze the impact of soil moisture on the nutrient infiltration efficiency of organic fertilizer, and the nutrient infiltration impact coefficient of the target field is determined based on the impact analysis results.

[0042] Based on the precipitation period distribution in the meteorological forecast data, assess the degree of time conflict risk between the fertilization timing and the precipitation period, and input the degree of time conflict risk into a preset risk level mapping library to obtain the time conflict risk level of the fertilization timing.

[0043] Based on the nutrient penetration impact coefficient and the time period conflict risk level, the input amount of the candidate organic fertilizer is adjusted in a coordinated manner to obtain the adjusted input amount of the candidate organic fertilizer.

[0044] The application time point is adjusted synchronously to avoid periods of high conflict risk, resulting in an adjusted application time point.

[0045] In a preferred embodiment, the formula for calculating the amount of candidate organic fertilizer input is as follows: ;

[0046] In the formula, This indicates the adjusted amount of the candidate organic fertilizer to be applied. This indicates the amount of the candidate organic fertilizer to be applied. This represents the nutrient permeability influence coefficient. This represents the preset correction factor for organic fertilizer application standards. This indicates the conflict risk level for the specified time period.

[0047] In a preferred embodiment, determining the specific fertilization schedule for the target field based on the adjusted input amount and application time, and integrating it into an organic fertilizer fertilization plan for the target field, includes:

[0048] Based on the correlation between the field zoning information, soil texture distribution data and the adjusted input amount of the target field, the differences in the ability of the soil texture of the target field to adsorb and retain organic fertilizer nutrients are analyzed, and the difference analysis results of the target field are obtained.

[0049] Based on the difference analysis results, corresponding application rates are assigned to different zones in the target field to obtain the zoned application rate data of the target field;

[0050] Based on the adjusted application time points, the distribution of field operation channels and crop planting density in the target field, the fertilization operation sequence and dedicated operation time periods for different zones in the target field are determined, and a zoned fertilization operation plan for the target field is generated.

[0051] By integrating the application rate data for each zone and the fertilization operation plan for each zone, an organic fertilizer application scheme for the target field is obtained.

[0052] To address the above problems, the present invention also provides an organic fertilizer application system, the system comprising:

[0053] The organic fertilizer data acquisition module is used to acquire crop fertilizer requirement data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers for the target field.

[0054] The dynamic nutrient balance analysis module is used to perform dynamic nutrient balance analysis on the crop fertilizer requirement data and the soil fertilizer supply data to obtain the nutrient demand gap data of the target crop during its growth cycle.

[0055] The organic fertilizer nutrient release curve fitting module is used to associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and to deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer.

[0056] The organic fertilizer supply and demand optimal parameter determination module is used to perform spatiotemporal matching analysis between the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer.

[0057] The fertilization parameter adaptation and adjustment module is used to adapt and adjust the input amount and application time of the candidate organic fertilizer according to the optimal application amount and the fertilization timing.

[0058] The fertilization plan formulation module is used to determine the specific fertilization arrangement for the target field based on the adjusted input amount and the application time, and integrate it into an organic fertilizer fertilization plan for the target field.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This invention integrates data on crop nutrient requirements, soil nutrient supply, and organic fertilizer characteristics to conduct dynamic nutrient balance analysis and dynamic nutrient release simulation. This enables precise spatiotemporal matching of nutrient demand gaps and organic fertilizer supply, accurately pinpointing key nutrient demand windows and optimal application rates, reducing nutrient supply and demand mismatches, improving crop nutrient absorption and utilization rates, and reducing resource waste.

[0061] 2. This invention dynamically adjusts fertilization parameters by combining real-time soil moisture and meteorological forecast data. Through the allocation of application rates by region and optimization of operation plans, it adapts to the heterogeneity of fields and field operation conditions. The resulting fertilization plan is more in line with the actual production scenario, ensuring the effectiveness of fertilization and helping to implement precision fertilization in modern agriculture. Attached Figure Description

[0062] Figure 1 This is a schematic flowchart of an organic fertilizer application method provided in an embodiment of the present invention;

[0063] Figure 2This is a functional block diagram of an organic fertilizer application system provided in an embodiment of the present invention;

[0064] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0065] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0066] This application provides an organic fertilizer application method. The execution entity of this organic fertilizer application method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the organic fertilizer application method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0067] Reference Figure 1 The diagram shown is a flowchart illustrating an organic fertilizer application method according to an embodiment of the present invention. In this embodiment, the organic fertilizer application method includes:

[0068] S1. Obtain crop fertilizer requirements data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers for the target field.

[0069] Representative plots with soil type, climate conditions, and cultivation patterns consistent with the target plots were selected as experimental fields, avoiding special areas to ensure uniformity. The experiment included nitrogen, phosphorus, and potassium (NPK) nutrients, each with four levels: no fertilization, low, medium, and high. These were divided into multiple independent plots arranged randomly, with protective rows between plots to prevent water and fertilizer interference. The target crop variety was planted, and all field management practices remained uniform throughout the growing season except for fertilization amount. After crop maturity, samples were harvested from each plot, dried, and pulverized to determine the NPK nutrient content. The total nutrient uptake of the entire plant was calculated based on yield to determine the crop's nutrient requirements.

[0070] Soil samples were collected from the topsoil layer at evenly distributed points in the target field. After removing impurities, the samples were mixed thoroughly, air-dried, ground, and sieved to obtain analytical samples. Organic matter content was determined using the potassium dichromate external heating method, followed by digestion titration. Available phosphorus was determined using the sodium bicarbonate extraction colorimetric method, with the content determined by colorimetric analysis after extraction and color development. Available potassium was determined using the ammonium acetate extraction method, followed by shaking filtration and detection using a flame photometer. Alkali-available nitrogen was determined using the diffusion method, with results obtained through distillation titration. Based on the measured values, combined with soil bulk density and topsoil thickness, the total amount of nutrients available from the soil was calculated, yielding soil fertility data.

[0071] Multiple sampling points were selected at different locations and layers of the candidate organic fertilizer compost pile. Equal amounts of samples were collected, crushed, and mixed thoroughly. Representative samples were obtained by quartering the samples, then air-dried, ground, and sieved for later use. Nitrogen content was determined using the Kjeldahl method, with results obtained through digestion, distillation, and titration. Phosphorus content was determined by acid digestion followed by precipitation and weighing, with the content confirmed by digestion, precipitation, drying, and weighing. Potassium content was determined by post-digestion flame photometry, with the digestion solution diluted and detected using a flame photometer. Based on the nitrogen, phosphorus, and potassium nutrient determination results, fertilizer supply data for the candidate organic fertilizers were compiled.

[0072] Soil samples from the target field and candidate organic fertilizer samples were mixed according to field ratios, placed in a culture container, and humidified to a suitable field capacity. The container was sealed, with ventilation holes left, and then placed in a constant-temperature incubator for stable cultivation. At multiple time points during the cultivation period, the culture container was removed, and available nitrogen, phosphorus, and potassium nutrients were extracted from the soil using corresponding chemical extraction methods. The content of each nutrient was determined using relevant measurement methods. Available nutrient data at different time points were recorded, and the rate of nutrient release and cumulative total were analyzed to clarify the nutrient release pattern of organic fertilizer in the soil and determine the mineralization characteristic parameters of candidate organic fertilizers.

[0073] The beneficial effects are that through scientific and standardized field trials, soil sampling and testing, organic fertilizer testing and mineralization cultivation processes, the nutrient requirements of crops, soil nutrient supply data, candidate organic fertilizer nutrient supply data and mineralization characteristic parameters of target fields can be accurately obtained. This provides comprehensive and reliable basic data support for the subsequent formulation of reasonable organic fertilizer application plans, ensuring the pertinence and effectiveness of fertilization and helping to coordinate crop growth and nutrient absorption.

[0074] S2. Perform dynamic nutrient balance analysis on the crop fertilizer requirement data and the soil fertilizer supply data to obtain the nutrient demand gap data of the target crop during its growth cycle;

[0075] In this embodiment of the invention, the step of performing dynamic nutrient balance analysis on the crop nutrient requirement data and the soil nutrient supply data to obtain nutrient demand gap data of the target crop during its growth cycle includes:

[0076] According to the growth cycle of the target crop, the crop fertilizer requirement data of the target field is divided into stage fertilizer requirement data for different growth stages;

[0077] Based on the soil physicochemical properties parameters in the soil fertility data of the target field, the basic nutrient supply capacity of the soil in the target field is evaluated to generate basic fertility intensity data of the soil in the target field at different growth stages.

[0078] The difference between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data is compared to analyze the nutrient supply and demand differences between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data in different growth stages, so as to obtain the stage nutrient requirement data of the target crop.

[0079] By integrating the nutrient requirement data for each stage, the nutrient gap data for the target crop during its growth cycle is obtained.

[0080] The step of assessing the soil's basic nutrient supply capacity based on soil physicochemical properties in the soil fertility data of the target field, to generate soil basic fertility intensity data for the target field at different growth stages, includes:

[0081] The soil fertility supply data of the target field is analyzed to obtain the soil physicochemical property parameters of the soil fertility supply data;

[0082] The soil physicochemical properties parameters are dynamically correlated with the growth stages of the target field to determine the nutrient supply influencing factors of the target field at different growth stages.

[0083] Based on the nutrient supply influencing factors, the soil physicochemical properties parameters are comprehensively evaluated to generate initial soil fertility potential data for the target field at different growth stages.

[0084] Based on the historical management records of the target field, the initial soil fertility potential data is corrected to obtain the basic soil fertility intensity data of the target field at different growth stages.

[0085] Regular field inspections are conducted to record the morphological changes of the target crop from sowing to maturity. Based on the physiological characteristics of crop growth and development, key growth stages such as emergence, seedling stage, jointing stage, flowering stage, fruiting stage, and maturity stage are identified, and the start and end times of each growth stage are determined. Referring to the physiological nutrient requirements of this crop variety and field trial data of similar crops, combined with the total nutrient uptake data from the target field's crop nutrient requirements data, the absorption ratios and rates of various nutrients such as nitrogen, phosphorus, and potassium at different growth stages are analyzed. According to the nutrient absorption ratios at each growth stage, the total nutrient uptake data from the crop nutrient requirements data is rationally allocated, clarifying the specific nutrient requirements for each growth stage, thereby obtaining stage-specific nutrient requirement data for different growth stages.

[0086] This study analyzes soil physicochemical properties, including soil organic matter content, available nitrogen, phosphorus, and potassium content, pH value, and soil texture. Soil organic matter content directly affects the soil's nutrient storage and release capacity; available nitrogen, phosphorus, and potassium content reflects the current level of nutrients that the soil can directly supply to crops; pH value affects nutrient availability; and soil texture determines the rate of soil nutrient retention and supply. Considering the root distribution depth, absorption capacity, and nutrient requirements of the target crop at different growth stages—for example, seedlings have shallow roots and weak absorption capacity, relying heavily on available nutrients—the basic soil fertility intensity should focus on the immediate supply of available nutrients; during the jointing and flowering stages, roots are well-developed and nutrient requirements are high, requiring the basic soil fertility intensity to consider both the nutrients released from organic matter mineralization and the continuous supply of available nutrients. By comprehensively analyzing the supporting role of various soil physicochemical properties in nutrient supply to crops at different growth stages, the study quantifies the intensity of various nutrients that the soil can stably provide at each growth stage, generating basic soil fertility intensity data for the target field at different growth stages.

[0087] For each growth stage of the target crop, the stage-specific nutrient requirement data and corresponding soil baseline nutrient supply intensity data are extracted, and compared one by one according to various nutrient types such as nitrogen, phosphorus, and potassium. For each nutrient, the amount of nutrient that the crop needs to absorb during that growth stage is determined, along with the actual nutrient supply corresponding to the soil's nutrient intensity at that stage. By directly comparing the stage-specific nutrient requirement and the corresponding supply at the soil baseline nutrient supply intensity for the same growth stage, the difference between the two is determined. If the stage-specific nutrient requirement is greater than the corresponding supply at the soil baseline nutrient supply intensity, the difference represents the deficiency of that nutrient type at that stage; if the stage-specific nutrient requirement is less than the corresponding supply at the soil baseline nutrient supply intensity, the difference represents the surplus of that nutrient type at that stage. The deficiencies or surpluses of various nutrients at each growth stage are systematically compiled and analyzed to determine the supply and demand matching of various nutrients at different growth stages, thus obtaining the stage-specific nutrient requirement data for the target crop.

[0088] Nutrient requirement data for all growth stages of the target crop were collected and categorized by type of nutrient, such as nitrogen, phosphorus, and potassium. The deficiencies of each nutrient type at each growth stage were accumulated. If a nutrient surplus existed at a certain growth stage, it was deducted during the accumulation process, ultimately yielding the total deficiency of all nutrients throughout the entire growth cycle. Simultaneously, the distribution of nutrient deficits across different growth stages was analyzed to identify the critical periods for nutrient demand and the magnitude of the deficits at each critical period. The total nutrient deficit and the distribution of deficits across different growth stages were integrated to form a complete nutrient requirement deficit data for the target crop throughout its growth cycle, encompassing both total amount and stage distribution.

[0089] Soil fertility data from the target field was analyzed, including measurements of soil nutrient content, structural characteristics, and pH levels. This data was categorized by attribute, distinguishing between core indicators directly reflecting soil fertility and auxiliary indicators affecting nutrient availability. For each data category, the corresponding measurement methods and result presentation formats were defined. For example, results obtained using the potassium dichromate external heating method were classified as soil organic matter content, while results obtained using the sodium bicarbonate extraction colorimetric method were classified as available phosphorus content. Through systematic organization and classification, specific indicators characterizing basic soil properties and fertility levels, such as soil organic matter content, available nitrogen, phosphorus, and potassium content, pH value, soil texture, and soil bulk density, were extracted from the soil fertility data, forming a complete set of soil physicochemical property parameters.

[0090] Identify the various growth stages of the target crop and, considering the physiological characteristics of each stage, analyze the mechanisms by which soil physicochemical parameters affect nutrient supply at each stage. For example, during the seedling stage, the root system is not yet fully developed, and the absorption capacity of available nutrients is weak. At this time, the content of available nitrogen, phosphorus, and potassium in the soil, as well as pH value, become key factors affecting nutrient supply. pH value affects the availability of nutrients by changing their form, and the content of available nutrients directly determines the amount of nutrients that can be absorbed. During the vigorous growth period of the crop, the root system is well-developed, and the demand for fertilizer is high. Soil organic matter content and soil texture become important influencing factors. Organic matter continuously releases nutrients through mineralization, and soil texture affects the retention and migration rate of nutrients. Analyze each growth stage individually, and screen out the soil physicochemical parameters that are directly related to and significantly affect nutrient supply at that stage, identifying them as the influencing factors of nutrient supply in the target field at different growth stages.

[0091] For each growth stage, the nutrient supply influencing factors were screened, and their respective weights in the nutrient supply process at that stage were first determined. Indicators directly providing nutrients, such as the content of available nitrogen, phosphorus, and potassium, had the highest weight; indicators affecting nutrient availability, such as pH value, had the second highest weight; and indicators affecting nutrient retention and release, such as soil texture and organic matter content, had the lowest weight. Based on the actual measurement results of each influencing factor, its quality level was determined. For example, high content of available nitrogen, phosphorus, and potassium was considered excellent; pH value within a suitable range was also excellent; and loam soil with high organic matter content was also excellent. The levels of each influencing factor were then comprehensively considered according to their weights. Excellent factors were quantified with a higher standard, medium-level factors with a medium standard, and poor-level factors with a lower standard. By comprehensively calculating the sum of the contribution values ​​of each factor, the potential nutrient supply capacity of the target field at that growth stage was obtained, generating initial soil fertility potential data for different growth stages.

[0092] Historical management records for the target field over the past three years were collected. This data included the types, timing, and total amount of fertilizers applied, irrigation frequency and volume, crop varieties and rotation, and types of pesticides used in pest and disease control. The long-term impact of these historical management practices on soil physicochemical properties was analyzed. For example, long-term application of organic fertilizer increases soil organic matter content, frequent excessive irrigation may lead to the loss of readily available nutrients, and continuous cropping may cause excessive consumption of specific nutrients. Initial soil fertility potential data for each growth stage was adjusted based on the impact of historical management records. If historically, reasonable application of organic fertilizer and a scientific crop rotation system resulted in good soil fertility, the initial data was appropriately increased. If long-term continuous cropping or excessive fertilization led to soil nutrient imbalance, the initial data was appropriately decreased according to the degree of imbalance. Through this targeted adjustment, the deviation between the initial soil fertility potential data and the actual soil fertility capacity was corrected, resulting in basic soil fertility intensity data for different growth stages that accurately reflect the actual situation of the target field.

[0093] The beneficial effects are that by accurately dividing the fertilizer requirements according to the crop growth stage, assessing the fertilizer supply intensity at different stages in combination with soil physicochemical properties, systematically comparing the differences in nutrient supply and demand and integrating the gaps throughout the entire cycle, the nutrient supply and demand matching of the target crop at each growth stage and throughout the entire growth period is clearly defined. This provides precise data support for the subsequent scientific formulation of organic fertilizer application plans, ensuring that fertilization not only meets the nutrient needs of crops at different growth stages, but also avoids nutrient waste or insufficient supply, improves the targeting and effectiveness of fertilization, and helps crops grow healthily and improve yield and quality.

[0094] S3. Associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer.

[0095] In this embodiment of the invention, the step of associating the mineralization characteristic parameters with soil environmental factors in the soil fertility data, and extrapolating the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available forms over time, to fit the nutrient release dynamic curve data of the candidate organic fertilizer, includes:

[0096] A coupling relationship analysis was conducted between the mineralization characteristic parameters in the candidate organic fertilizers and the soil environmental factors in the soil fertility data to determine the dominant environmental factors affecting the nutrient mineralization process of the target field and their corresponding influence weights.

[0097] Based on the mineralization characteristic parameters, the dominant environmental factors and their corresponding influence weights, the mineralization and decomposition process of the candidate organic fertilizer in the soil environment of the target field over time is simulated to obtain the available nutrient concentration data of the candidate organic fertilizer.

[0098] Based on the available nutrient concentration data, the instantaneous release rate and cumulative release amount of nutrients in the candidate organic fertilizer at different discrete time points are deduced;

[0099] The instantaneous release rate data and the cumulative release amount data are subjected to nonlinear fitting to obtain the nutrient release dynamic curve data of the candidate organic fertilizer.

[0100] Soil environmental factors influencing the mineralization process of organic fertilizer were extracted from soil fertility data, including soil pH, organic matter content, soil texture, soil moisture, and soil aeration. These indicators were derived from previous measurements of soil in the target field and directly reflect the soil's physicochemical and environmental conditions. The mineralization characteristic parameters of candidate organic fertilizers were systematically correlated with these soil environmental factors. A comparative analysis was conducted using the controlled variable method, i.e., keeping other environmental factors constant while changing the level of only one environmental factor, and observing the changes in mineralization characteristic parameters such as mineralization rate and nutrient release. The influence of each environmental factor on the mineralization process was analyzed one by one. By comparing the magnitude of changes in mineralization characteristic parameters under different environmental factors, the environmental factor with the largest change was identified as the dominant environmental factor that plays a decisive role in the mineralization process. For example, when the soil pH deviates from the suitable range, the mineralization rate decreases significantly, and this factor becomes the dominant environmental factor. Further analysis was conducted on the differences in the degree of influence of the dominant environmental factor on the mineralization characteristic parameters at different levels. The greater the degree of influence, the higher the influence weight. By comprehensively evaluating the contribution of the dominant environmental factor to the mineralization process, its corresponding influence weight was determined.

[0101] Based on the actual soil environment of the target field, a simulated soil environment system was constructed in the laboratory. Candidate organic fertilizer was thoroughly mixed with the target field soil according to the actual field application ratio and placed in a culture container. Based on the determined dominant environmental factors and their corresponding influence weights, the environmental conditions of the culture system were set to ensure that the levels of the dominant environmental factors were consistent with those of the target field soil. For example, if the dominant environmental factors were soil moisture and pH, the moisture content of the culture system was controlled within the appropriate range of the target field's field water holding capacity, and the pH was adjusted to the actual measured value of the target field, while also considering the stability of other non-dominant environmental factors. During the cultivation process, samples were taken strictly at set time intervals. At each sampling, an appropriate amount of the soil-organic fertilizer mixture was taken from the culture container, and the available nutrients were separated using corresponding chemical extraction methods. For example, available phosphorus was extracted using sodium bicarbonate extraction, available potassium using ammonium acetate extraction, and available nitrogen using diffusion. The concentration of available nutrients in each sample was determined using appropriate measurement methods. The measurement results at different time points were organized chronologically to obtain the data on the concentration of available nutrients of the candidate organic fertilizer under the target field soil environment over time.

[0102] The obtained available nutrient concentration data are arranged in chronological order of sampling time to determine the available nutrient concentration value corresponding to each discrete time point. For instantaneous release rate data, the difference in available nutrient concentration between two adjacent discrete time points is calculated. This difference represents the amount of nutrient released between the two time points. This release amount is then divided by the interval between the two time points to obtain the average instantaneous release rate within this time period. This method is used to calculate each adjacent time interval, ultimately forming the instantaneous release rate data for different discrete time points. For cumulative release data, based on the available nutrient concentration at the initial time point, the difference in available nutrient concentration between each subsequent time point and the previous time point is sequentially accumulated. That is, the cumulative release amount at each discrete time point equals the cumulative release amount at the previous time point plus the concentration difference between the current time point and the previous time point. This calculation is performed step by step in chronological order to obtain the cumulative release amount data corresponding to each discrete time point.

[0103] Prepare a coordinate plotting tool and establish two two-dimensional coordinate systems with time as the horizontal axis and instantaneous release rate and cumulative release amount as the vertical axes, respectively. Plot the instantaneous release rate data and cumulative release amount data at different discrete time points on the two coordinate systems, ensuring each data point accurately corresponds to its time and value. Observe the distribution trend of the instantaneous release rate data points. Generally, the release rate of organic fertilizer nutrients is relatively fast in the initial stage, gradually slowing down and stabilizing over time, showing a distribution characteristic of steepening at first and then leveling off. The cumulative release amount data points show a distribution characteristic of gradually increasing and eventually leveling off. Based on the distribution trend of the data points, connect each data point with a smooth curve, ensuring that the curve fits the vast majority of data points accurately, reflecting the overall change pattern of the data and avoiding deviation from key data points. This method completes nonlinear fitting, obtaining dynamic curve data of instantaneous release rate and dynamic curve data of cumulative release amount that can intuitively reflect the change pattern of nutrient release of candidate organic fertilizer over time; these are collectively referred to as the nutrient release dynamic curve data of candidate organic fertilizer.

[0104] The beneficial effects are as follows: by coupling analysis of mineralization characteristic parameters of candidate organic fertilizers with soil environmental factors, the dominant environmental factors and their weights affecting nutrient mineralization can be accurately identified. Combined with the actual soil environment to simulate the mineralization process, the concentration data of available nutrients can be clearly obtained. Further, the instantaneous release rate and cumulative release data can be derived. Finally, a nutrient release dynamic curve is formed through nonlinear fitting. This comprehensively and accurately reveals the nutrient release pattern of candidate organic fertilizers in the target field soil, providing a reliable basis for subsequent scientific matching of crop nutrient requirements and the formulation of precise organic fertilizer application plans. This ensures that the release of organic fertilizer nutrients is synchronized with crop absorption, improves fertilizer utilization, reduces nutrient loss, and takes into account both crop growth needs and soil ecological protection.

[0105] S4. Perform spatiotemporal matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer.

[0106] In this embodiment of the invention, the step of performing spatiotemporal matching analysis between the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer includes:

[0107] Based on the growth stage of the target crop, the nutrient demand gap data is decomposed into a time dimension to generate stage nutrient demand data corresponding to different growth stages of the target crop.

[0108] By analyzing the nutrient release dynamic curve data according to the same time dimension as the nutrient requirement data of the aforementioned stage, the expected organic fertilizer nutrient supply data corresponding to the target crop at different growth stages can be obtained.

[0109] The nutrient requirement data for each stage is compared and analyzed with the corresponding expected organic fertilizer nutrient supply data to obtain the nutrient supply and demand difference data of the target crop at different growth stages.

[0110] Based on the nutrient supply and demand difference data, the nutrient supply effect of the candidate organic fertilizer at the preset application rate is evaluated, and the preset application rate is adjusted according to the nutrient supply effect to obtain the preliminary application rate data of the candidate organic fertilizer.

[0111] Identify the growth stage with the most significant differences in the nutrient supply and demand data, and define the growth stage as the critical nutrient requirement window period for the target crop;

[0112] Based on the preliminary application data and the critical fertilizer requirement window, the optimal application rate and timing of the candidate organic fertilizer are determined.

[0113] The process of combining the preliminary application data with the critical fertilizer requirement window to determine the optimal application rate and timing of the candidate organic fertilizer includes:

[0114] Based on the nutrient supply and demand difference data corresponding to the critical fertilizer demand window, determine the amount of nutrient gap that needs to be supplemented by the initial application amount data during the critical fertilizer demand window;

[0115] Based on the nutrient content data of the candidate organic fertilizer and the expected nutrient release rate data of the candidate organic fertilizer during the critical nutrient demand window, the incremental amount of organic fertilizer used by the candidate organic fertilizer to fill the nutrient gap is determined.

[0116] The incremental organic fertilizer is allocated to the initial application data to obtain the optimal application rate of the candidate organic fertilizer;

[0117] Based on the optimal application rate and the time required for nutrient release to reach the effective supply peak in the nutrient release dynamic curve data, the starting point of the critical fertilizer demand window is shifted forward to determine the fertilization timing of the candidate organic fertilizer.

[0118] Identify the various growth stages of the target crop from emergence to maturity, including emergence, seedling stage, jointing stage, flowering stage, fruiting stage, and maturity stage. Clearly define the specific time range of each growth stage to ensure that the time dimension is fully matched with the crop's physiological development process. Referring to the nutrient absorption patterns of the target crop at each growth stage, and combining this with the total nutrient deficit data, analyze the priority and proportion of nitrogen, phosphorus, potassium, and other nutrients required at different growth stages. Based on the nutrient requirement proportions and time span of each growth stage, rationally allocate the total nutrient deficit to each growth stage. For example, if the flowering stage has a high demand for phosphorus and potassium, allocate a larger deficit to that stage. Identify the specific deficit values ​​for each type of nutrient within each growth stage to generate stage-specific nutrient requirement data for the target crop at different growth stages.

[0119] Based on the time range of each growth stage of the target crop, a time-dimensional correspondence is established to ensure that the time interval of the nutrient release dynamic curve data is completely consistent with the time interval of the nutrient requirement data for each stage. For the instantaneous release rate curve and cumulative release curve in the nutrient release dynamic curve data, curve data within the time interval corresponding to each growth stage are extracted. The cumulative release curve directly reflects the total amount of nutrients that organic fertilizer can release at that stage. The curve data within each growth stage's time interval are integrated, using the cumulative release amount within that interval as the core basis, combined with the changing trend of the instantaneous release rate, to determine the total amount of nutrients that organic fertilizer can actually provide to the crop at that growth stage, thus obtaining the expected organic fertilizer nutrient supply data for the target crop at different growth stages.

[0120] The nutrient requirements data for each growth stage are paired with the corresponding expected organic fertilizer nutrient supply data by nutrient type, ensuring direct comparison between the two sets of data for the same nutrient and the same growth stage. For each pair of data, the difference between the stage nutrient requirement and the expected organic fertilizer nutrient supply is calculated to determine whether the supply of that nutrient type at that growth stage meets the demand. If the stage nutrient requirement is greater than the expected supply, the difference is considered a nutrient deficiency; if the stage nutrient requirement is less than the expected supply, the difference is considered a nutrient surplus. The system then systematically organizes the comparison results of all paired data according to the growth stage sequence and nutrient type, forming complete nutrient supply and demand difference data for the target crop at different growth stages.

[0121] An evaluation standard for nutrient supply effectiveness is established: a good supply effect is defined as when the nutrient supply and demand difference data for each growth stage are within a reasonable range, with no significant supply-demand imbalance. Based on this standard, the nutrient supply and demand difference data under the current preset application rate is analyzed. If there is a significant nutrient deficiency at a certain growth stage, the preset application rate is too low and needs to be increased proportionally to the deficiency. If there is a significant nutrient surplus, the preset application rate is too high and needs to be reduced proportionally to the surplus. After adjusting the preset application rate, the nutrient release dynamic curve data is re-analyzed to obtain new expected organic fertilizer nutrient supply data. This data is then compared with the stage nutrient demand data until the nutrient supply and demand difference data meets the evaluation standard. The application rate at this point is the initial application rate data corresponding to the target crop.

[0122] Nutrient supply and demand data for each growth stage were analyzed systematically, comparing the magnitude of the supply and demand differences for various nutrients at each stage. Special attention was paid to growth stages with significant nutrient deficiencies, as these stages directly impact crop growth and yield. By comparing the degree of difference across different growth stages, the absolute value of the supply and demand difference for each stage was calculated. The growth stage with the largest absolute value was identified as having the most significant nutrient supply and demand difference. Combined with the crop's physiological characteristics at this growth stage, it was determined whether it was a critical period for crop growth and development. For example, significant nutrient deficiency during flowering would directly affect pollination results, indicating a critical nutrient requirement at this stage. This growth stage was then defined as the critical nutrient requirement window for the target crop.

[0123] Taking into account the rationality of the initial application rate data and the nutrient requirements of the critical fertilizer requirement window, if there is a slight nutrient deficiency during the critical fertilizer requirement window, the total application rate can be appropriately increased based on the initial application rate to ensure sufficient nutrient supply during the window. If the nutrient supply during the window is sufficient under the initial application rate, the total application rate can be maintained unchanged, with a focus on optimizing the timing of fertilization. The timing of fertilization is determined based on the time node of the critical fertilizer requirement window. If it is applied as basal fertilizer, the organic fertilizer should be applied to the soil before sowing to ensure that nutrient release reaches its peak when the window arrives. If it is applied as topdressing, it should be applied some time before the critical fertilizer requirement window to allow the organic fertilizer sufficient time for mineralization and decomposition, ensuring a precise match between nutrient release and the window's requirements. By adjusting the application rate and optimizing the timing of fertilization, the optimal application rate and timing of candidate organic fertilizers that meet the nutrient requirements of each growth stage and conform to the nutrient release patterns of organic fertilizers are ultimately determined.

[0124] Nutrient supply and demand difference data corresponding to the critical fertilizer requirement window period were extracted. From this, the stage nutrient requirement data and the corresponding expected organic fertilizer nutrient supply data for that window period were selected. Both sets of data are derived from previous stage-by-stage comparative analysis and directly reflect the nutrient supply and demand matching situation during the critical window period. The stage nutrient requirement data is defined as the total amount of nutrients necessary for crop growth during the critical window period, while the expected organic fertilizer nutrient supply data is the total amount of nutrients that organic fertilizer can provide during that window period at the initial application rate. By directly comparing the values ​​of these two sets of data, the stage nutrient requirement data for the critical window period is subtracted from the corresponding expected organic fertilizer nutrient supply data. The difference obtained is the portion of nutrients that cannot be met by the initial application rate data during the critical fertilizer requirement window period, which is the nutrient gap that needs to be supplemented.

[0125] Nutrient content data from previously measured candidate organic fertilizers was retrieved. This data represents the total content of various nutrients, including nitrogen, phosphorus, and potassium, in the candidate organic fertilizers, determined through laboratory chemical analysis. The expected nutrient release rate corresponding to the critical nutrient requirement window was extracted from the nutrient release dynamic curve data. This data represents the proportion of nutrients that the candidate organic fertilizer can release during the critical window period relative to its total nutrient content, obtained by analyzing the ratio of the cumulative release amount to the total nutrient content during that window. The additional nutrient gap was taken as the target value to be filled. This target value was divided by the product of the nutrient content of the candidate organic fertilizer and the expected nutrient release rate during the critical nutrient requirement window. The result is the specific amount of candidate organic fertilizer required to accurately fill this nutrient gap, i.e., the incremental amount of candidate organic fertilizer.

[0126] The incremental organic fertilizer application is identified as a supplementary amount to address the additional nutrient gap during the critical nutrient requirement window, compensating for any insufficient supply from the initial application during this period. Since the initial application data already meets the nutrient requirements for all growth stages except the critical window, and the incremental organic fertilizer application is solely for filling the additional gap during the critical window, a direct summation method is used for allocation. The initial application data and the calculated incremental organic fertilizer application are combined; the sum of these two amounts represents the candidate organic fertilizer application rate that satisfies the basic nutrient requirements of the target crop at all growth stages while precisely filling the additional nutrient gap during the critical nutrient requirement window, thus yielding the optimal application rate of the candidate organic fertilizer.

[0127] In-depth analysis of nutrient release dynamic curve data clearly presents the complete process of candidate organic fertilizers in the soil environment, from the initial release of nutrients to the peak effective supply. By observing the trend of the curve, the specific time from the start of nutrient release to the peak release is determined. This time is the mineralization and decomposition time required for organic fertilizer nutrients to form an effective supply peak after application to the soil. Using the starting point of the critical nutrient requirement window as a benchmark, this starting point is shifted forward by the determined time. The shifted time point ensures that after the organic fertilizer is applied to the soil, after the necessary mineralization and decomposition process, its nutrient release peak occurs precisely within the critical nutrient requirement window, thus achieving precise synchronization between nutrient supply and crop demand. This shifted time point is the optimal application time for candidate organic fertilizers.

[0128] The beneficial effects are as follows: by breaking down nutrient demand gaps according to growth stages, simultaneously analyzing organic fertilizer nutrient supply data, and comparing supply and demand differences stage by stage, the nutrient matching situation at each stage is accurately determined. The initial application rate is optimized based on the supply and demand differences, while the key nutrient demand window period with the most significant nutrient supply and demand differences is identified. Finally, the optimal application rate and appropriate fertilization timing are determined comprehensively, achieving a precise match between organic fertilizer nutrient release and crop needs at each stage, especially the needs during key window periods. This avoids both insufficient nutrient supply affecting crop growth and nutrient surplus causing waste and soil burden, significantly improving the utilization efficiency of organic fertilizer, providing stable and sufficient nutrient support for the healthy growth of crops throughout their entire growth period, and helping to improve crop yield and quality.

[0129] S5. Adjust the amount and timing of application of the candidate organic fertilizer according to the optimal application rate and the timing of fertilization.

[0130] In this embodiment of the invention, the step of adapting and adjusting the input amount and application time of the candidate organic fertilizer according to the optimal application rate and the fertilization timing includes:

[0131] Acquire real-time soil moisture data of the target field and meteorological forecast data of the target crop during its growth cycle;

[0132] The real-time soil moisture data is correlated with the optimal application rate to analyze the impact of soil moisture on the nutrient infiltration efficiency of organic fertilizer, and the nutrient infiltration impact coefficient of the target field is determined based on the impact analysis results.

[0133] Based on the precipitation period distribution in the meteorological forecast data, assess the degree of time conflict risk between the fertilization timing and the precipitation period, and input the degree of time conflict risk into a preset risk level mapping library to obtain the time conflict risk level of the fertilization timing.

[0134] Based on the nutrient penetration impact coefficient and the time period conflict risk level, the input amount of the candidate organic fertilizer is adjusted in a coordinated manner to obtain the adjusted input amount of the candidate organic fertilizer.

[0135] The application time point is adjusted synchronously to avoid periods of high conflict risk, resulting in an adjusted application time point.

[0136] The formula for calculating the amount of candidate organic fertilizer to be applied is as follows:

[0137] ;

[0138] In the formula, This indicates the adjusted amount of the candidate organic fertilizer to be applied. This indicates the amount of the candidate organic fertilizer to be applied. This represents the nutrient permeability influence coefficient. This represents the preset correction factor for organic fertilizer application standards. This indicates the conflict risk level for the specified time period.

[0139] Multiple monitoring locations were evenly distributed across the target field, avoiding areas with rocks and dense root systems. Soil moisture sensors were vertically inserted into the topsoil, connected to the data logger, ensuring tight contact between the sensor and soil. The data logger automatically collected soil moisture data at fixed time intervals. During the data collection process, the sensor's operational status was periodically checked, and surface soil impurities were cleaned to avoid affecting data accuracy. Simultaneously, personnel periodically calibrated the sensor data in the field using a drying and weighing method. Soil samples were collected from selected monitoring locations, the wet soil mass was weighed, and the samples were dried in an oven to constant weight. The actual soil moisture content was calculated and compared with the sensor data to correct for discrepancies. The soil moisture data was transmitted in real-time through the data logger, and compiled into real-time soil moisture data for the target field. Meteorological forecast data was obtained from medium- and long-term forecasts issued by authoritative meteorological departments, focusing on collecting key meteorological information such as precipitation periods, precipitation amounts, temperature, and sunshine duration during the target crop's growth cycle. The forecast data was processed and filtered, removing invalid information to form complete meteorological forecast data.

[0140] Real-time soil moisture data was categorized into three levels based on soil moisture content: excessively dry, suitable, and excessively wet. Suitable soil moisture ensures the smooth penetration of organic fertilizer nutrients into the crop root zone, while excessively dry soil hinders nutrient penetration, and excessively wet soil may lead to nutrient loss. A correlation analysis was performed between different moisture levels and the optimal application rate for nutrient penetration. Under the same application rate, the nutrient penetration depth and distribution range were compared between suitable, excessively dry, and excessively wet soils. Excessively dry soil showed shallow nutrient penetration and a narrow distribution range, while excessively wet soil showed uneven nutrient penetration and easy loss. Suitable soil moisture resulted in the best nutrient penetration effect. Based on the degree of influence of different moisture levels on nutrient penetration efficiency, corresponding nutrient penetration influence coefficients were determined. The influence coefficient for suitable soil moisture served as the baseline value, while the influence coefficients for excessively dry and excessively wet soils were set according to the degree of decrease in penetration efficiency. The more significant the decrease in penetration efficiency, the lower the influence coefficient, thus quantifying the impact of soil moisture on nutrient penetration.

[0141] The distribution of precipitation periods in meteorological forecast data is analyzed to determine the start time, duration, and expected precipitation amount for each precipitation period. The determined fertilization timing is compared with each precipitation period to determine if there is any overlap. If the fertilization timing and precipitation period completely overlap and the expected precipitation amount is large, the risk of time conflict is high; if there is partial overlap or the expected precipitation amount is small, the risk is medium; if there is no overlap, the risk is low. A preset risk level mapping library is established based on the impact of fertilization and precipitation conflicts on fertilization effectiveness, including high, medium, and low risk levels. Each level corresponds to a specific conflict scenario description. For example, the high-risk level corresponds to "fertilization timing completely overlaps with heavy precipitation periods," the medium-risk level corresponds to "fertilization timing partially overlaps with weak precipitation periods," and the low-risk level corresponds to "fertilization timing does not overlap with precipitation periods." The assessed time conflict risk level is matched with scenarios in the preset risk level mapping library; the scenario description that matches the risk level corresponds to the conflict risk level for that time period.

[0142] Adjustment rules were formulated based on the nutrient penetration impact coefficient and the time-period conflict risk level. If the nutrient penetration impact coefficient was at the baseline value and the time-period conflict risk level was low, it indicated that the current soil and meteorological conditions were suitable for fertilization, and no adjustment was needed to the amount of candidate organic fertilizer applied. If the nutrient penetration impact coefficient was lower than the baseline value, the application amount needed to be appropriately increased according to the degree of decrease in the coefficient to compensate for the reduced effective nutrient supply caused by insufficient nutrient penetration efficiency. For example, excessively dry soil required an increased application amount to ensure sufficient nutrients could penetrate to the root zone. If the time-period conflict risk level was medium, the application amount needed to be slightly increased to address potential nutrient loss due to rainfall. If the risk level was high and the nutrient penetration impact coefficient was low, the proportion of the increased application amount needed to be comprehensively considered to both compensate for insufficient penetration and address the risk of rainfall loss, while avoiding excessive application that could lead to waste. Following these adjustment rules, the optimal application amount of candidate organic fertilizer was specifically adjusted based on the actual nutrient penetration impact coefficient and the time-period conflict risk level, resulting in the adjusted application amount of candidate organic fertilizer.

[0143] Referring to the distribution of precipitation periods and the risk level of period conflicts in meteorological forecast data, if the risk level corresponding to the original fertilization time is high, it is necessary to check the periods without precipitation before and after the precipitation period and choose the period closest to the original fertilization time that is without precipitation and has suitable soil moisture as the adjustment direction. For example, if the original fertilization time coincides with a period of heavy precipitation, the fertilization time can be moved forward to a period without precipitation before the precipitation to ensure that the organic fertilizer has enough time to infiltrate and avoid direct erosion by precipitation. If there is no suitable time before precipitation, it can be postponed until after the precipitation ends and the soil moisture recovers to a suitable range before fertilization. If the risk level is medium, the fertilization time can be slightly adjusted to avoid the core area of ​​the precipitation period. For example, if the original fertilization time partially overlaps with the precipitation period, it can be moved forward or postponed to 1-2 days before or after the start or end of the precipitation. During the adjustment process, it is necessary to combine the dynamic curve data of nutrient release to ensure that the adjusted application time can still match the peak of organic fertilizer nutrient release with the critical fertilizer requirement window of the crop, and finally obtain the adjusted application time that avoids the period of high conflict risk and conforms to the nutrient supply law.

[0144] The adjusted candidate organic fertilizer input amount is derived from the previous candidate organic fertilizer input amount obtained based on nutrient supply and demand differences and key fertilizer demand windows. This amount represents the application rate after preliminary optimization. The nutrient penetration impact coefficient is derived by analyzing the impact of different soil moisture conditions on organic fertilizer nutrient penetration efficiency by correlating real-time soil moisture data of the target field with the optimal application rate. Soil moisture is classified into excessively dry, suitable, and excessively wet levels. The coefficient is determined based on the degree of change in penetration efficiency by comparing the depth and distribution range of nutrient penetration under different levels. The preset organic fertilizer application benchmark correction coefficient is derived from past practical experience in organic fertilizer application in different fields, combined with the summary of nutrient supply effects under different fertilization scenarios. This is a pre-set fixed correction value used to adapt to various adjustment scenarios. The time period conflict risk level is derived by assessing the degree of time conflict risk between fertilization timing and precipitation periods based on the precipitation period distribution in meteorological forecast data. This risk level is then matched with scenarios in a preset risk level mapping library to obtain the corresponding risk level.

[0145] The significance of the formula is that, by combining the nutrient penetration impact coefficient and the time-period conflict risk level, it can specifically modify the candidate organic fertilizer input amount before adjustment, thereby obtaining the adjusted candidate organic fertilizer input amount that is suitable for the current soil moisture and meteorological conditions of the target field. Specifically, the formula uses the nutrient penetration impact coefficient to reflect the influence of soil moisture on the nutrient penetration efficiency of organic fertilizer, and uses the time-period conflict risk level to link the impact of the conflict between the rainfall period and the fertilization timing on nutrient supply. By integrating these two aspects, the formula applies the input amount before adjustment, so that the modified input amount can both compensate for insufficient nutrient penetration caused by abnormal soil moisture and cope with the risk of nutrient loss caused by rainfall conflicts.

[0146] When soil moisture is suitable, the nutrient infiltration impact coefficient will be relatively high. If the time-related conflict risk level is low, the adjusted candidate organic fertilizer application rate will be close to the original rate. When soil moisture deviates from suitable conditions and the nutrient infiltration impact coefficient decreases, the adjusted candidate organic fertilizer application rate will increase to compensate for the reduced effective nutrient supply caused by decreased nutrient infiltration efficiency. When the time-related conflict risk level increases, the corresponding correction value will decrease, and the adjusted candidate organic fertilizer application rate will increase accordingly to address potential nutrient loss due to rainfall conflicts. When the nutrient infiltration impact coefficient is low and the time-related conflict risk level is high, the adjusted candidate organic fertilizer application rate will increase more significantly to simultaneously compensate for insufficient infiltration and address the risk of loss.

[0147] The beneficial effects are as follows: by acquiring real-time soil moisture data of the target field and meteorological forecast data during the crop growth cycle, and combining the impact of soil moisture on the nutrient penetration efficiency of organic fertilizer, the nutrient penetration impact coefficient is determined. Based on the distribution of precipitation periods, the conflict risk level between fertilization timing and precipitation is assessed. Based on the synergistic adjustment of both, the amount of organic fertilizer input and the application time are adjusted. This not only makes up for the insufficient nutrient penetration caused by abnormal soil moisture, but also avoids the nutrient loss caused by high conflict risk precipitation periods. This ensures that the amount of organic fertilizer input is precisely matched with the soil environment and meteorological conditions, and the application time is highly consistent with the crop's nutrient requirements. This further improves the scientific nature and pertinence of fertilization, ensures the efficient use of organic fertilizer nutrients, provides a stable and suitable nutrient supply for crop growth, and reduces resource waste and environmental impact.

[0148] S6. Based on the adjusted input amount and application time, determine the specific fertilization arrangement for the target field and integrate it into an organic fertilizer fertilization plan for the target field.

[0149] In this embodiment of the invention, determining the specific fertilization arrangement for the target field based on the adjusted input amount and the application time, and integrating it into an organic fertilizer fertilization plan for the target field, includes:

[0150] Based on the correlation between the field zoning information, soil texture distribution data and the adjusted input amount of the target field, the differences in the ability of the soil texture of the target field to adsorb and retain organic fertilizer nutrients are analyzed, and the difference analysis results of the target field are obtained.

[0151] Based on the difference analysis results, corresponding application rates are assigned to different zones in the target field to obtain the zoned application rate data of the target field;

[0152] Based on the adjusted application time points, the distribution of field operation channels and crop planting density in the target field, the fertilization operation sequence and dedicated operation time periods for different zones in the target field are determined, and a zoned fertilization operation plan for the target field is generated.

[0153] By integrating the application rate data for each zone and the fertilization operation plan for each zone, an organic fertilizer application scheme for the target field is obtained.

[0154] Obtain field zoning information for the target field. This information divides the target field into multiple relatively independent zones based on topographical continuity and field management units. Each zone has clear boundaries and facilitates individual fertilization operations. The zoning must cover the entire field without omissions. Collect soil texture distribution data for each zone. By evenly selecting multiple sampling points within each zone, soil samples are collected by vertically inserting a soil auger into the topsoil. The samples are placed on a clean plastic sheet, and the soil texture type is determined by touch, rubbing, and kneading to clearly distinguish different texture categories such as clay, loam, and sand. The adjusted input amount was correlated with the soil texture of each zone. Soil samples from each zone were thoroughly mixed with candidate organic fertilizers in the same proportion, placed in the same container, and left to stand for a fixed time under the same environmental conditions. Then, the corresponding nutrient determination methods were used to detect the organic fertilizer nutrient content retained in each soil sample. The nutrient retention values ​​of different texture zones were compared to clarify the specific differences in nutrient adsorption and retention capacity of clay zones, sandy soil zones, and loam zones. The difference analysis results of the target field were obtained.

[0155] Based on the nutrient adsorption and retention capacity corresponding to soil texture in each zone according to the difference analysis results, for the clay zone, due to its strong nutrient adsorption and retention capacity, nutrients applied with organic fertilizer are not easily lost with water and can be supplied to crops for a long time. Therefore, the application amount allocated to this zone is slightly reduced by a fixed proportion based on the adjusted input amount to avoid excessive nutrient accumulation. For the sandy soil zone, due to its weak nutrient adsorption and retention capacity, nutrients applied with organic fertilizer are easily lost with irrigation or rainfall. Therefore, the application amount allocated to this zone is slightly increased by a fixed proportion based on the adjusted input amount to ensure that crops can obtain sufficient nutrients. For the loam zone, due to its moderate nutrient adsorption and retention capacity, which can stably retain and supply nutrients, the corresponding application amount is directly allocated according to the adjusted input amount. The final determined application amount for each zone is recorded one by one and mapped to the specific zone to obtain the zone application amount data for the target field.

[0156] Using the adjusted application time as the core operation period, the distribution of field operation channels in the target fields was analyzed, and the connection between each zone and the operation channel was clarified. Zones closer to the operation channels were prioritized for the early stages of the operation period, facilitating the rapid entry and exit of fertilizer transport vehicles and fertilization equipment, reducing transportation and equipment relocation time. Crop planting density distribution data for each zone was collected, and the number of crop plants per unit area in each zone was counted in the field to identify zones with higher planting densities. These zones require more meticulous operation during fertilization to avoid crop damage, and therefore, longer dedicated operation periods were allocated to them to ensure smooth operation. Combining the connection between each zone and the operation channel and the operation time requirements corresponding to the planting density, the fertilization operation sequence for each zone was determined sequentially. At the same time, the dedicated start and end times for each zone were clearly marked, generating a zoned fertilization operation plan for the target fields that includes the operation sequence, time period, and corresponding zones.

[0157] The application rate data for each zone was integrated with the zone-specific fertilization operation plan, marking the corresponding application rate for each zone. The operation sequence and designated time period for each zone in the plan were also matched. Furthermore, key operational points for fertilization in each zone were added. For example, after fertilization in clay zones, light tillage is required, with the tillage depth controlled within the topsoil layer to promote the full integration of organic fertilizer nutrients with the soil. After fertilization in sandy soil zones, light watering is necessary, just enough to moisten the surface soil, avoiding excessive watering that could lead to nutrient loss. This information was then organized into coherent content by zone, covering the specific application rate, corresponding operation sequence, designated time period, and targeted operational points for each zone. This ensured that fertilization operations in each zone had clear guidelines and standards, resulting in an organic fertilizer fertilization plan for the target field.

[0158] The beneficial effects include: by combining the zoning information of the target field, soil texture distribution, and adjusted input amounts, the differences in nutrient adsorption and retention capacity of each zone are clarified. Based on this, appropriate application rates are allocated to different zones, matching the strong retention capacity of clay zones to avoid excessive nutrient accumulation, and adapting to the weak retention capacity of sandy soil zones to ensure sufficient nutrient supply. Furthermore, by combining the distribution of field access routes and crop planting density, a reasonable zoning fertilization operation sequence and dedicated time periods are determined, reducing equipment transfer time and reserving sufficient operation time for zones with higher planting density to avoid crop damage. Finally, by integrating zoning application rate data and zoning fertilization operation plans, and supplementing them with targeted operational points, the resulting organic fertilizer fertilization program allows each zone's fertilization to be precisely adapted to its own soil conditions and operational needs, ensuring the stability and rationality of nutrient supply. At the same time, it standardizes field operation procedures, improves the orderliness and efficiency of operations, and reduces resource waste and operational errors.

[0159] like Figure 2The diagram shown is a functional block diagram of an organic fertilizer application system provided in an embodiment of the present invention.

[0160] The organic fertilizer application system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the organic fertilizer application system 100 may include an organic fertilizer data acquisition module 101, a dynamic nutrient balance analysis module 102, an organic fertilizer nutrient release curve fitting module 103, an organic fertilizer supply and demand optimal parameter determination module 104, a fertilization parameter adaptation and adjustment module 105, and a fertilization plan formulation module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0161] In this embodiment, the functions of each module / unit are as follows:

[0162] The organic fertilizer data acquisition module 101 is used to acquire crop fertilizer requirement data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of the target field.

[0163] The dynamic nutrient balance analysis module 102 is used to perform dynamic nutrient balance analysis on the crop fertilizer requirement data and the soil fertilizer supply data to obtain the nutrient demand gap data of the target crop during its growth cycle.

[0164] The organic fertilizer nutrient release curve fitting module 103 is used to associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and to deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer.

[0165] The organic fertilizer supply and demand optimal parameter determination module 104 is used to perform spatiotemporal matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application amount and timing of the candidate organic fertilizer.

[0166] The fertilizer parameter adaptation and adjustment module 105 is used to adapt and adjust the input amount and application time of the candidate organic fertilizer according to the optimal application amount and the fertilization timing.

[0167] The fertilization plan formulation module 106 is used to determine the specific fertilization arrangement of the target field based on the adjusted input amount and the application time, and integrate it into the organic fertilizer fertilization plan of the target field.

[0168] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0169] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0171] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0172] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for applying organic fertilizer, characterized in that, The method includes: S1. Obtain crop fertilizer requirements data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers for the target field. S2. Perform dynamic nutrient balance analysis on the crop nutrient requirement data and the soil nutrient supply data to obtain nutrient gap data of the target crop during its growth cycle, including: Based on the soil physicochemical properties parameters in the soil fertility data of the target field, the basic nutrient supply capacity of the soil in the target field is assessed to generate basic nutrient supply intensity data of the target field at different growth stages, including: The soil fertility supply data of the target field is analyzed to obtain the soil physicochemical property parameters of the soil fertility supply data; The soil physicochemical properties parameters are dynamically correlated with the growth stages of the target field to determine the nutrient supply influencing factors of the target field at different growth stages. Based on the nutrient supply influencing factors, the soil physicochemical properties parameters are comprehensively evaluated to generate initial soil fertility potential data for the target field at different growth stages. Based on the historical management records of the target field, the initial soil fertility potential data is corrected to obtain the soil basic fertility intensity data of the target field at different growth stages; S3. Associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer. S4. Perform spatiotemporal matching analysis on the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer. S5. Based on the optimal application rate and the fertilization timing, adjust the input amount and application time of the candidate organic fertilizer accordingly, including: Acquire real-time soil moisture data of the target field and meteorological forecast data of the target crop during its growth cycle; The real-time soil moisture data is correlated with the optimal application rate to analyze the impact of soil moisture on the nutrient infiltration efficiency of organic fertilizer, and the nutrient infiltration impact coefficient of the target field is determined based on the impact analysis results. Based on the precipitation period distribution in the meteorological forecast data, assess the degree of time conflict risk between the fertilization timing and the precipitation period, and input the degree of time conflict risk into a preset risk level mapping library to obtain the time conflict risk level of the fertilization timing. Based on the nutrient penetration impact coefficient and the time period conflict risk level, the input amount of the candidate organic fertilizer is adjusted in a coordinated manner to obtain the adjusted input amount of the candidate organic fertilizer. The calculation formula for the input amount of the candidate organic fertilizer is as follows: ； In the formula, This indicates the adjusted amount of the candidate organic fertilizer to be applied. This indicates the amount of the candidate organic fertilizer to be applied. This represents the nutrient permeability influence coefficient. This represents the preset correction factor for organic fertilizer application standards. Indicates the conflict risk level of the aforementioned time period; The application time point is adjusted synchronously to avoid periods of high conflict risk, resulting in an adjusted application time point; S6. Based on the adjusted input amount and application time, determine the specific fertilization arrangement for the target field and integrate it into an organic fertilizer fertilization plan for the target field.

2. The method for applying organic fertilizer as described in claim 1, characterized in that, The dynamic nutrient balance analysis of the crop nutrient requirement data and the soil nutrient supply data to obtain nutrient gap data of the target crop during its growth cycle includes: According to the growth cycle of the target crop, the crop fertilizer requirement data of the target field is divided into stage fertilizer requirement data for different growth stages. The difference between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data is compared to analyze the nutrient supply and demand differences between the stage fertilizer requirement data and the soil basic fertilizer supply intensity data in different growth stages, so as to obtain the stage nutrient requirement data of the target crop. By integrating the nutrient requirement data for each stage, the nutrient gap data for the target crop during its growth cycle is obtained.

3. The method for applying organic fertilizer as described in claim 1, characterized in that, The process of associating the mineralization characteristic parameters with soil environmental factors in the soil fertility data, and extrapolating the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available forms over time, to fit the nutrient release dynamic curve data of the candidate organic fertilizer, includes: A coupling relationship analysis was conducted between the mineralization characteristic parameters in the candidate organic fertilizers and the soil environmental factors in the soil fertility data to determine the dominant environmental factors affecting the nutrient mineralization process of the target field and their corresponding influence weights. Based on the mineralization characteristic parameters, the dominant environmental factors and their corresponding influence weights, the mineralization and decomposition process of the candidate organic fertilizer in the soil environment of the target field over time is simulated to obtain the available nutrient concentration data of the candidate organic fertilizer. Based on the available nutrient concentration data, the instantaneous release rate and cumulative release amount of nutrients in the candidate organic fertilizer at different discrete time points are deduced; The instantaneous release rate data and the cumulative release amount data are subjected to nonlinear fitting to obtain the nutrient release dynamic curve data of the candidate organic fertilizer.

4. The method for applying organic fertilizer as described in claim 1, characterized in that, The step of performing spatiotemporal matching analysis between the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer includes: Based on the growth stage of the target crop, the nutrient requirement gap data is decomposed into a time dimension to generate stage nutrient requirement data for the target crop at different growth stages. By analyzing the nutrient release dynamic curve data according to the same time dimension as the nutrient requirement data of the aforementioned stage, the expected organic fertilizer nutrient supply data corresponding to the target crop at different growth stages can be obtained. The nutrient requirement data for each stage is compared and analyzed with the corresponding expected organic fertilizer nutrient supply data to obtain the nutrient supply and demand difference data of the target crop at different growth stages. Based on the nutrient supply and demand difference data, the nutrient supply effect of the candidate organic fertilizer at the preset application rate is evaluated, and the preset application rate is adjusted according to the nutrient supply effect to obtain the preliminary application rate data of the candidate organic fertilizer. Identify the growth stage with the most significant differences in the nutrient supply and demand data, and define the growth stage as the critical nutrient requirement window period for the target crop; Based on the preliminary application data and the critical fertilizer requirement window, the optimal application rate and timing of the candidate organic fertilizer are determined.

5. The method for applying organic fertilizer as described in claim 4, characterized in that, The process of combining the preliminary application data with the critical fertilizer requirement window to determine the optimal application rate and timing of the candidate organic fertilizer includes: Based on the nutrient supply and demand difference data corresponding to the critical fertilizer demand window, determine the amount of nutrient gap that needs to be supplemented by the initial application amount data during the critical fertilizer demand window; Based on the nutrient content data of the candidate organic fertilizer and the expected nutrient release rate data of the candidate organic fertilizer during the critical nutrient demand window, the incremental amount of organic fertilizer used by the candidate organic fertilizer to fill the nutrient gap is determined. The incremental organic fertilizer is allocated to the initial application data to obtain the optimal application rate of the candidate organic fertilizer; Based on the optimal application rate and the time required for nutrient release to reach the effective supply peak in the nutrient release dynamic curve data, the starting point of the critical fertilizer demand window is shifted forward to determine the fertilization timing of the candidate organic fertilizer.

6. The method for applying organic fertilizer as described in claim 1, characterized in that, The step of determining the specific fertilization schedule for the target field based on the adjusted input amount and application time, and integrating it into an organic fertilizer fertilization plan for the target field, includes: Based on the correlation between the field zoning information, soil texture distribution data and the adjusted input amount of the target field, the differences in the ability of the soil texture of the target field to adsorb and retain organic fertilizer nutrients are analyzed, and the difference analysis results of the target field are obtained. Based on the difference analysis results, corresponding application rates are assigned to different zones in the target field to obtain the zoned application rate data of the target field. Based on the adjusted application time points, the distribution of field operation channels and crop planting density in the target field, the fertilization operation sequence and dedicated operation time periods for different zones in the target field are determined, and a zoned fertilization operation plan for the target field is generated. By integrating the application rate data for each zone and the fertilization operation plan for each zone, an organic fertilizer application scheme for the target field is obtained.

7. An organic fertilizer application system, characterized in that, The system for implementing the organic fertilizer application method according to any one of claims 1-6, the system comprising: The organic fertilizer data acquisition module is used to acquire crop fertilizer requirement data, soil fertilizer supply data, and fertilizer supply data and mineralization characteristic parameters of candidate organic fertilizers for the target field. The dynamic nutrient balance analysis module is used to perform dynamic nutrient balance analysis on the crop fertilizer requirement data and the soil fertilizer supply data to obtain the nutrient demand gap data of the target crop during its growth cycle. The organic fertilizer nutrient release curve fitting module is used to associate the mineralization characteristic parameters with the soil environmental factors in the soil fertilization data, and to deduce the release rate and cumulative release amount of nutrients in the candidate organic fertilizer as they transform into available form over time, so as to fit the nutrient release dynamic curve data of the candidate organic fertilizer. The organic fertilizer supply and demand optimal parameter determination module is used to perform spatiotemporal matching analysis between the nutrient demand gap data and the nutrient release dynamic curve data to determine the optimal application rate and timing of the candidate organic fertilizer. The fertilization parameter adaptation and adjustment module is used to adapt and adjust the input amount and application time of the candidate organic fertilizer according to the optimal application amount and the fertilization timing. The fertilization plan formulation module is used to determine the specific fertilization arrangement for the target field based on the adjusted input amount and the application time, and integrate it into an organic fertilizer fertilization plan for the target field.

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